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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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The analysis of soft error in static random access memory and mitigation by using transmission gate Kadir, Farhana Mohamad Abdul; Julai, Norhuzaimin
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7664

Abstract

As the progress of technology continues in accordance to Moore’s law, the density and downsizing of circuitry presents a significant vulnerability to the effects of soft errors. This study proposed a novel method to mitigate soft errors by increasing the robustness of complementary metal oxide semiconductor (CMOS) technology against soft errors via the use of transmission gates within the memory nodes of static random access memory (SRAM) which functioned as a low pass filter that disallowed the occurrence of data corruption. The proposed SRAM was tested against parameter variation of supply voltage and temperature. The critical charge was observed to increase with supply voltage increase, with the opposite being true of the increase in temperature. The increase in critical charge of up to 88.63% was achieved with regards to parameter variation for the transmission gate SRAM in comparison to the 6T SRAM.
New developments and trends in 5G technologies: applications and concepts Tyokighir, Silas Soo; Mom, Joseph; Ukhurebor, Kingsley Eghonghon; Igwue, Gabriel
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6032

Abstract

Fifth-generation (5G) wireless technology is the most up-to-date iteration of mobile data networks. This research analyzes the effectiveness of next-generation mobile networks in tandem with mobile communication technologies. Various difficulties encountered at each stage are discussed. With the advent of 5G networks, users may connect to the internet at lightning speeds from almost any location. 5G is one of a kind because of its new characteristics, which allow it to link people and enable them to operate gadgets, machines, and things. 5G mobile technology’s varying speed and capabilities will allow for new user experiences and link new businesses. Companies must know where they can best put 5G to use. This research paper examines and analyzes various topics in great detail, demonstrating how mmWave, massive multiple-input and multiple-output (massive MIMO), microcells, mobile edge computing (MEC), beamforming, diverse antenna technologies, and so on can all work together to improve cellular networks. The primary goals of this article are to demonstrate some of the most recent technical developments and to analyze potential future research directions for the 5G mobile system.
Enhancing speech emotion recognition with deep learning using multi-feature stacking and data augmentation Al Mukarram, Khasyi; Mukhlas, M. Anang; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6049

Abstract

This study evaluates the effectiveness of data augmentation on 1D convolutional neural network (CNN) and transformer models for speech emotion recognition (SER) on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset. The results show that data augmentation has a positive impact on improving emotion classification accuracy. Techniques such as noising, pitching, stretching, shifting, and speeding are applied to increase data variation and overcome class imbalance. The 1D CNN model with data augmentation achieved 94.5% accuracy, while the transformer model with data augmentation performed even better at 97.5%. This research is expected to contribute better insights for the development of accurate emotion recognition methods by using data augmentation with these models to improve classification accuracy on the RAVDESS dataset. Further research can explore larger and more diverse datasets and alternative model approaches.
Comparative field assessment of grounding enhancement material for electrical earthing system Zhe Kang, Lim; Chun Lim, Siow; Muhammad, Usman; Aman, Fazlul; Nor, Normiza Mohamad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7303

Abstract

Grounding enhancement material (GEM) is used to lower the earthing resistance value of a given earthing system. In this paper, a commercially available GEM is experimented at the field alongside with Sodium Chloride, Copper II Sulphate and planting soil. The well established Wenner’s 4 pole method and fall of potential method was employed to measure the soil resistivity and earthing resistance respectively. It was found that the salts i.e., Sodium Chloride and Copper II Sulphate are superior in reducing the earthing resistance as reduction of more than 85% were observed. However, the commercial GEM has exhibited the most stable earthing resistance value over a period of 101 days, exhibiting the lowest standard deviation. This seems to suggest that the commercial GEM has superior moisture retention capability. This study also proven that Sodium Chloride can be dissolved by heavy downpour and replenishing it periodically is needed in a tropical country like Malaysia with regular thunderstorms and heavy downpours.
A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries Joko Prasetyo, Sri Yulianto; Simanjuntak, Bistok Hasiholan; Susatyo, Yeremia Alfa; Sulistyo, Wiwin
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5910

Abstract

This study aims to build a computer model to detect built-up land in the identified tsunami hazard zone based on Sentinel 2A imagery using the normalized built up area index (NBI), urban index (UI), normalize difference build-up index (NDBI), a modified built-up index (MBI), index-based builtup index (IBI) algorithms, optimized with machine learning Random Forest (RF) and extreme gradient boosting (XGboost) algorithms and the spatial patterns are predicted using the ordinary kriging (OK) method. Testing of the accuracy of the classification and optimization results was performed using the Kohen Kappa and overall accuracy functions. The results of the study show that a built-up land consisting of open land and water, settlements, industry areas, and agriculture and tourism areas can be identified using the parameters of built-up indices. The accuracy testings that were performed using overall accuracy and Kohen Kappa methods show that classification and prediction are highly accurate using XGboost machine learning, namely 91%. This study produces a novelty of finding, namely a computer model to detect and predict the spatial distribution of built-up land in 4 scales, i.e., very low, low, high, and very high based on NBI, UI, NDBI, MBI, IBI data extracted from Sentinel 2A imagery.
Revisiting 5G quality of service in Bangkok metropolitan region: BTS Skytrain station areas Daengsi, Therdpong; Sriamorntrakul, Pakkasit; Chatchalermpun, Surachai; Phanrattanachai, Kritphon
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7337

Abstract

This article compares two of the leading mobile network operators (MNOs) in Thailand’s telecom market in terms of the service quality of Thailand’s fifth generation (5G) networks. The following three factors: download speed, upload speed, and latency, which are frequently considered to be indicators of the quality of Internet networks, were examined. The researchers employed the test results to determine the quality of service (QoS) that was achieved by comparing newly collected data to data that had previously been examined utilizing the same format and application in the middle of May 2021. The average download speed decreased from 196.4 Mbps in 2021 to 140.4 Mbps in 2023, while the average upload speed dropped from 62.6 Mbps in 2021 to 52.0 Mbps in 2023. Furthermore, the average latency increased from 14.9 ms in 2021 to 23.3 ms in 2023. These results show a considerably enhanced service although the test region in this study only comprised BTS stations. Furthermore, this was the case even though the test area in this study only encompassed a small percentage of the total population.
Enhanced building footprint extraction from satellite imagery using Mask R-CNN and PointRend NourEldeen, Ahmed; E. Wahed, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7443

Abstract

The extraction of building footprints from aerial photos and satellite imagery plays a crucial role in change detection, urban development, and detecting encroachments on agricultural land. Deep neural networks offer the capability of extracting features and provide accurate methods for detecting and extracting building footprints from satellite imagery. Image segmentation, the process of dividing an image into coherent parts, can be accomplished using two types: semantic segmentation and instance segmentation. Convolutional neural networks (CNN) are commonly used for both instance and semantic segmentation tasks. In this paper, we propose a hybrid approach to extracting building footprints from low-resolution satellite imagery using instance segmentation techniques. Our analysis demonstrates that the mask region-based CNN (R-CNN) architecture with a ResNet-34 backbone and PointRend head to improve the bounding-boxes and mask prediction achieves the highest performance, as evidenced by various metrics, including an average precision (AP) score of 83.39% and an F-1 score of 85.71%. This approach holds promise for developing automated tools to process satellite imagery, benefiting fields such as agriculture, land use monitoring, and disaster response.
Simplifying the electronic wedge brake system model through model order reduction techniques Che Hasan, Mohd Hanif; Hassan, Mohd Khair; Ahmad, Fauzi; Marhaban, Mohammad Hamiruce; Haris, Sharil Izwan; Arasteh, Ehsan
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5815

Abstract

The electronic wedge brake (EWB) uses self-reinforcement principles to optimise stopping power, but its mathematical model has various actuation angles and system dynamics making controller design complex and computationally burdensome. Therefore, the model order reduction (MOR) is made based on three factors that may have a negligible influence on the EWB system: the motor inductance, lead screw axial damping, and wedge mass. Six reduced order model (ROM) types were proposed when one, two, or all factors were ignored. The ROM accuracy was analysed using the frequency and time domain. The percentage of root means square error (RMSE) response value between the EWB benchmark model, and the predicted response based on the ROM was found to be less than 2%, with ROM size reduced from 5 to 2 orders. It guarantees that the new ROM series will be useful for simpler EWB controller design. The proposed ROM simplifies the original model drastically while retaining accuracy at an adequate level. Even though the simplest EWB model is a 2nd  order linear system, the best ROM vary depending on EWB design parameters.
An interior penalty function method for solving fuzzy nonlinear programming problems Govindhasamy, Vanaja; Kandasamy, Ganesan
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7047

Abstract

In this article, we investigate fuzzy interior penalty function method for solving fuzzy nonlinear programming problems (FNLPP) based on a new fuzzy arith-metic, unconstrained optimization, and fuzzy ranking on the parametric form of triangular fuzzy numbers (TFN). The main objective of this paper is to solve constrained fuzzy nonlinear programming problems using interior penalty func-tions (IPF) by converting it into unconstrained optimization problems. We prove an important lemma and a convergence theorem for the interior penalty functions method. Interior penalty function techniques favor sites near the boundary of the feasible region in the interior. We present a numerical example of the suggested method and compare the results to those produced by existing methods.
Real-time object detection and distance measurement for humanoid robot using you only look once Dwijayanti, Suci; Suprapto, Bhakti Yudho; Mutiyara, Mutiyara; Rendyansyah, Rendyansyah
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7476

Abstract

Humanoid robots are designed to mimic human structures and utilize cameras to process visual input to identify surrounding objects. However, previous studies have focused solely on object detection, overlooking both the complexities of real-world implementation and the significance of calculating the distance between objects and the robot. This study proposes a system that employs the you only look once (YOLO) algorithm to detect various objects in the proximity of a robot. Using a dataset of primary data collected in a laboratory, the detected objects are from 12 classes, including humans, chairs, tables, cabinets, computers, books, doors, bottles, eggs, learning modules, cups, and hands, with each class comprising 1500 data points. Two YOLO architectures, namely tiny YOLOv3 and tiny YOLOv4, are assessed for their performance in object detection, with the tiny YOLOv4 demonstrating a superior accuracy of 82.99% compared to tiny YOLOv3. Evaluation under simulated conditions yields an accuracy of 74.16%, while in real-time scenarios, accuracies are 61.66% under bright conditions and 38.33% under dim conditions, affirming tiny YOLOv4’s efficacy. Moreover, this study reveals an average error distance of 31% between an object and the robot in real-time conditions. The developed system enhances human–robot interaction capabilities via data transmission.

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